The Global Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Shang-Ming's work contributes towards the following SDG(s):
About Shang-Ming
Welcome Prospective PhD Students
Shangming is interested in supervising strong potential UK and international PhD students from allied health professionals or computing science backgrounds. The areas of PhD studies include health data science, health and biomedical informatics, artificial intelligience (AI) for healthcare and medicine, quantitative data analysis and/or evaluation of e-health technologies, such as
- AI in health and care;
- explainable machine learning (XAI) in healthcare;
- ethical AI in healthcare;
- electronic health records analytics;
- natural language processing /text mining in healthcare;
- e-health technology transformation;
- early detection and diagnosis;
- multimorbidity and polypharmacy;
- disease phenotyping;
- patient safety;
- etc.
If you have an ambition in pursuing a PhD study in any related topic, you are most welcome to contact him via the email.
About
Currently, Shangming is the Deputy Director of the Centre for Health Technology at the Faculty of Health: Medicine, Dentistry and Human Sciences. He is also the Director of NHS Kernow Datalab, and an affiliated investigator with the Health Data Research UK(HDR UK). His research was funded by HDRUK, MRC, EPSRC, HCRW, Charities, and international collaborations. Before joining the University of Plymouth, Shangming worked with the Scottish Digital Health and Care Institute and University of Strathclyde, Swansea University, De Montford University, University of Essex, and Chinese Academy of Sciences.
His primary scholarly interests are AI in health and biomedical informatics, health data science, biomedical statistics and information aggregation / integration via type-1 OWA operators and type-2 OWA operators. In implementation science, he is particularly interested in (big) data analytics and AI with electronic health data for personalised medicine, disease phenotyping, polypharmacy, multimorbidity, risk factors identification etc; clinical decision supports driven by type-1 OWA operators and type-2 OWA operators; machine learning and data mining applied to epidemiology and public health. In developmental domains, he is particularly interested in developing and using explainable/transparent machine learning (i.e. XAI), type-1/ type-2 OWA operators and other AI technologies for electronic health records and –omics data to extract personally useful information, such as rules and patterns, concerning lifestyles and health conditions to promote healthier lifestyles and prevent disease.
The medical conditions to which he is particularly interested in applying AI and biomedical statistics techniques include, but are not limited to, the long-term health conditions (such as cancer, dementia, epilepsy, asthma, diabetes, multiple sclerosis, mental health conditions etc.)
His areas of expertise:
- Artificial intelligence in health & care
- Machine learning /deep learning for health data analytics
- Health informatics
- Explainable AI
- Epidemiology
- Population health
- Big data analytics
- Medical statistics
- Data linkage (of electronic health records)
- Information aggregation/integration
- Biomedical signal processing
- Data mining and knowledge discovery
- Computational intelligence
He was the recipient of “ Best Paper Award ” sponsored by Springer Nature at the International Conference on Frontiers of Intelligent Computing: Theory and Applications; “ Best Poster Priz e” at the Royal College of Physicians (RCP) Annual Conference; IFIP-WG8.9 “ Outstanding Academic Service Award "; and “ Outstanding Reviewer Award " from Journal of Biomedical Informatics; Journal of Science and Medicine in Sport; Fuzzy Sets and Systems; IEEE Transactions on Cybernetics; Applied Soft Computing, Knowledge Based Systems, Expert Systems with Applications, respectively.
Supervised Research Degrees
PhD Students
- Sherif Shazly (2023~2025)‒ “Machine learning-based prediction of endometrial cancer prognosis” (Director of Study) .
- Xu Wang (2022~2025) ‒ “Improving Medication Verification for Cancer Patients: An AI Led Population Health Study” (Director of Study) .
- Xiatian Fan (2023~2027) ‒ “Mining Routinely Collected Electronic Health Records to Identify Effective Dietetic Factors for Optimal Care in General Practice” (Director of Study) .
- Tristan Coombe (2023~2029) ‒ “On the Use of Artificial Intelligence in Nursing Education” (Director of Study) .
- Joan Jonathan Mnyambo (2023~2027) ‒“Prediction of Diagnostic Accuracy using Artificial Intelligence and Big Data Analytics from HeroRats for Tuberculosis Detection” (Second Supervisor)
Teaching
Shangming’s teaching interests focused on the following areas:- Machine Learning for Healthcare
- Health Data Analytics
- Health Statitics
- Health Informatics & Digital Health
- Research Methods and Ethics
Advanced Concepts in Research: Methodology and Methods (APP758)
MSc Dissertation and Research Skills ( PROJ518)
